Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction

2026-07-17

AI Summary

The relationship between structure and charge carrier mobility in organic mixed ionic–electronic conductors remains poorly understood, largely due to the complexity and high noise levels inherent in nanobeam electron diffraction data, which hinder conventional analysis. To address this challenge, this work introduces machine learning into polymer 4D scanning transmission electron microscopy (4D-STEM) data analysis for the first time. By training models on synthetic datasets, the approach automatically identifies diffraction peak positions and intensities in semicrystalline polymers. Compared to traditional peak-detection algorithms, the proposed method significantly enhances both speed and accuracy across nearly all scenarios, enabling near-real-time structural visualization. This advancement provides a powerful new tool for elucidating structure–property relationships in these complex functional materials.

Abstract

Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.

Problem

• structure-property relationship
• semicrystalline polymers
• nanobeam electron diffraction
• organic mixed ionic electronic conductors
• 4DSTEM

Innovation

• machine learning
• nanobeam electron diffraction
• 4DSTEM
• polymer crystallinity
• structure-property relationship